Stanford researchers at the Bao Research Group have developed a second-generation stretchable multi-sensor tag technology for detecting physiological signals.
Stanford researchers have discovered that ribonucleoside vanadyl complexes can be used as an additive in transcription reactions resulting in ~2-fold increased yield.
Heart rhythm disorders are difficult to treat with conventional drug therapy and intraoperative injury to the cardiac conduction system (CCS) complicates heart-related surgeries and is a major cause of morbidity and mortality.
Stanford researchers have formulated a risk scoring calculator using a human induced pluripotent stem cell (iPSC) model to accurately predict and calculate insulin resistance via a novel blood test.
Using a novel convolutional neural network architecture, PlexusNet can be used for histologic image analysis with smaller parameter and training sets than current state-of-the-art models.
Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
Coronary artery bypass grafting (CABG) surgery is performed on nearly half a million patients with multivessel or diffuse coronary artery disease each year in the United States.
Stanford researches have formulated a robust database called PRECOG (Prediction of Clinical Outcomes from Genomics) that connects cancer genome expression and patient survival/outcomes in a more predictive and extensive collection than any other signature on the market.
This invention is a set of structures and associated processes to integrate GaN with Diamond to develop a full complementary CMOS device capable of operation in high power and high temperature applications.